Full waveform inversion based on hybrid gradient

The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber components in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial lowfrequency inversion to ensure the stability of the inversion,and use more high-wavenumber components in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.

Several Variants of the Primal-Dual Hybrid Gradient Algorithm with Applications

By reviewing the primal-dual hybrid gradient algorithm(PDHG)pro-posed by He,You and Yuan(SIAM J.Image Sci.,7(4)(2014),pp.2526–2537),in this paper we introduce four improved schemes for solving a class of saddle-point problems.Convergence properties of the proposed algorithms are ensured based on weak assumptions,where none of the objective functions are assumed to be strongly convex but the step-sizes in the primal-dual updates are more flexible than the pre-vious.By making use of variational analysis,the global convergence and sublinear convergence rate in the ergodic/nonergodic sense are established,and the numer-ical efficiency of our algorithms is verified by testing an image deblurring problem compared with several existing algorithms.

Hybrid gradient vector fields for path-following guidance

Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

Functionally graded structure of a nitride-strengthened Mg_(2)Si-based hybrid composite

The ex-situ incorporation of the secondary SiC reinforcement,along with the in-situ incorporation of the tertiary and quaternary Mg_(3)N_(2) and Si_(3)N_(4) phases,in the primary matrix of Mg_(2)Si is employed in order to provide ultimate wear resistance based on the laser-irradiation-induced inclusion of N_(2) gas during laser powder bed fusion.This is substantialized based on both the thermal diffusion-and chemical reactionbased metallurgy of the Mg_(2)Si–SiC/nitride hybrid composite.This study also proposes a functional platform for systematically modulating a functionally graded structure and modeling build-direction-dependent architectonics during additive manufacturing.This strategy enables the development of a compositional gradient from the center to the edge of each melt pool of the Mg_(2)Si–SiC/nitride hybrid composite.Consequently,the coefficient of friction of the hybrid composite exhibits a 309.3%decrease to–1.67 compared to–0.54 for the conventional nonreinforced Mg_(2)Si structure,while the tensile strength exhibits a 171.3%increase to 831.5 MPa compared to 485.3 MPa for the conventional structure.This outstanding mechanical behavior is due to the(1)the complementary and synergistic reinforcement effects of the SiC and nitride compounds,each of which possesses an intrinsically high hardness,and(2)the strong adhesion of these compounds to the Mg_(2)Si matrix despite their small sizes and low concentrations.

HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT

The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.

NEW HYBRID CONJUGATE GRADIENT METHOD AS A CONVEX COMBINATION OF LS AND FR METHODS

In this paper, we present a new hybrid conjugate gradient algorithm for unconstrained optimization. This method is a convex combination of Liu-Storey conjugate gradient method and Fletcher-Reeves conjugate gradient method. We also prove that the search direction of any hybrid conjugate gradient method, which is a convex combination of two conjugate gradient methods, satisfies the famous D-L conjugacy condition and in the same time accords with the Newton direction with the suitable condition. Furthermore, this property doesn't depend on any line search. Next, we also prove that, moduling the value of the parameter t,the Newton direction condition is equivalent to Dai-Liao conjugacy condition.The strong Wolfe line search conditions are used.The global convergence of this new method is proved.Numerical comparisons show that the present hybrid conjugate gradient algorithm is the efficient one.

A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered.This problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated learning.We propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost functions.We show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of iterations.We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum.We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

Hybrid vector beams with non-uniform orbital angular momentum density induced by designed azimuthal polarization gradient

Based on angular amplitude modulation of orthogonal base vectors in common-path interference method, we propose an interesting type of hybrid vector beams with unprecedented azimuthal polarization gradient and demonstrate in experiment. Geometrically, the configured azimuthal polarization gradient is indicated by intriguing mapping tracks of angular polarization states on Poincaré sphere, more than just conventional circles for previously reported vector beams. Moreover, via tailoring relevant parameters, more special polarization mapping tracks can be handily achieved. More noteworthily, the designed azimuthal polarization gradients are found to be able to induce azimuthally non-uniform orbital angular momentum density, while generally uniform for circle-track cases, immersing in homogenous intensity background whatever base states are. These peculiar features may open alternative routes for new optical effects and applications.

MPPT for Hybrid Energy System Using Gradient Approximation and Matlab Simulink Approach

This paper applies new maximum-power-point tracking （MPPT） algorithm to a hybrid renewable energy system that combines both Wind-Turbine Generator （WTG） and Solar Photovoltaic （PV） Module （SPVM）. In this paper, the WTG is a direct-drive system and includes wind turbine, three-phase permanent magnet synchronous generator, three-phase full bridge rectifier, and buck-bust converter, while the SPVM consist of solar PV modules, buck converter, maximum power tracking system for both systems, and load. Several methods are applied to obtain maximum performances, the appropriate and most effective method is called gradient-approximation method for WTG approach, because it enables the generator to operate at variable wind speeds. Furthermore MPPT also is used to optimized the achieved energy generated by solar PV modules.Matlab / Simulink approach is used to simulate, discuss, and optimized the generated power by varying the duty cycle of the converters, and tip speed ratio of the WTG system.

基于差分隐私的联邦学习方案

联邦学习的特点之一是进行训练的服务器并不直接接触数据,因此联邦学习本身就具有保护数据安全的特性。但是研究表明,联邦学习在本地数据训练和中心模型聚合等方面均存在隐私泄露的问题。差分隐私是一种加噪技术,通过加入适当噪声达到攻击者区分不出用户信息的目的。文中研究了一种基于本地和中心差分隐私的混合加噪算法(LCDP-FL),该算法能根据各个客户端不同权重、不同隐私需求,为这些客户端提供本地或混合差分隐私保护。而且我们证明该算法能够在尽可能减少计算开支的同时,为用户提供他们所需的隐私保障。在MNIST数据集和CIFAR-10数据集上对该算法进行了测试,并与本地差分隐私(LDP-FL)和中心差分隐私(CDP-FL)等算法进行对比,结果显示该混合算法在精确度、损失率和隐私安全方面均有改进,其算法性能最优。

基于TD3-PER的氢燃料电池混合动力汽车能量管理策略研究

为优化氢燃料电池混合动力汽车的燃料经济性及辅助动力电池性能,提出了一种基于优先经验采样的双延迟深度确定性策略梯度(TD3-PER)能量管理策略。采用双延迟深度确定性策略梯度(TD3)算法,在防止训练过优估计的同时实现了更精准的连续控制;同时结合优先经验采样(PER)算法,在获得更好优化性能的基础上加速了策略的训练。仿真结果表明:相较于深度确定性策略梯度(DDPG)算法,所提出的TD3-PER能量管理策略的百公里氢耗量降低了7.56%,平均功率波动降低了6.49%。

小型无人有缆遥控水下机器人智能控制方法

针对深度确定性策略梯度(DDPG)算法应用于无人有缆遥控水下机器人(ROV)运动控制时存在的坏样本影响学习稳定性、缺少环境探索能力以及学习时间长难收敛等问题,从神经网络结构、噪声引入和融合监督学习3个方面对DDPG算法进行改进,并提出了基于混合神经网络结构和参数噪声的监督式DDPG算法。仿真结果表明,监督式DDPG算法比常规DDPG算法和传统比例-积分-微分(PID)算法更加有效。

基于梯度搜索与进化机制的多目标混合算法

多目标进化算法(MOEA)因其良好的全局探索能力备受关注,但其在最优值附近的局部搜索能力却相对较弱,且对于具有大规模决策变量的优化问题,MOEA所需的种群数量与迭代次数都十分庞大,优化效率较低。基于梯度的优化算法能够很好地克服这些问题,但梯度搜索算法很难应用于多目标问题(MOPs)。在加权平均梯度的基础上引入随机权函数,发展多目标梯度算子,将其与基于参考点的第三代非支配排序遗传算法(NSGA-Ⅲ)结合,发展了多目标梯度优化算法(MOGBA)和多目标混合进化算法(HMOEA)。HMOEA在保留NSGA-Ⅲ良好的全局探索能力的同时,极大地增强了局部搜索能力。数值实验表明:HMOEA对于各种Pareto阵面都具有优秀的捕获能力,与典型的多目标算法相比效率提升了5~10倍。进一步将HMOEA应用于RAE2822翼型的多目标气动优化问题中,得到了理想的Pareto前沿,表明HMOEA是一种高效的优化算法,在气动优化设计中具有潜在的应用价值。

TPMS点阵结构的密度梯度杂交优化设计

三周期极小曲面(triply periodic minimal surface,TPMS)点阵结构因其优异的综合性能受到中外学者的广泛关注。在点阵结构实际应用过程中,常常需要对其进行优化设计以兼顾轻量化与承载性能两方面的要求。目前,对TPMS点阵结构的优化设计主要集中于密度梯度层面,未综合考虑载荷方向对其力学性能的影响。为此,首先研究了TPMS点阵结构的各向异性特征。基于平均场均匀化方法求解了不同类型TPMS点阵结构的等效弹性矩阵,通过Matlab插值计算,绘制了其在三维空间范围内的杨氏模量图。发现不同类型的TPMS点阵结构呈现出不同的各向异性特征,其中W点阵结构在[100]等轴线方向上性能较强,在[111]等斜向对角方向上性能较弱,而P点阵结构则刚好相反。根据TPMS点阵结构的各向异性,同时考虑主应力方向以及相对密度分布对其性能的影响,提出了TPMS点阵结构的密度梯度杂交优化设计方法。以悬臂梁模型为基础,基于载荷边界条件对其进行拓扑优化设计,并将拓扑优化密度云映射为点阵结构的相对密度分布,从而实现密度梯度设计。根据TPMS点阵结构的各向异性特征以及单元主应力方向分别选择W和P点阵单胞填充悬臂梁,使主应力方向位于点阵结构性能较强的方向,避免点阵结构在性能薄弱的方向承受较大的应力。将不同类型的TPMS点阵单元合理分布后,利用激活函数将它们进行杂交连接,实现结构梯度设计。综合相对密度分布和单元结构分布,生成密度梯度杂交点阵结构。采用有限元仿真方法对比分析优化设计前后点阵结构的承载性能,结果表明密度梯度W和P点阵结构的刚度与对应的均质点阵结构相比都有明显提高,而由W和P两种点阵单胞组成的密度梯度杂交点阵结构刚度最大,比密度梯度W和P点阵结构分别提高4.63%和33.63%。该结果表明在密度优化的基础上,根据承载时单元主应力方向将不同类型的点阵结构进行合理分布以及混合杂交设计能够进一步提高结构的整体刚度。建立的TPMS点阵结构密度梯度杂交优化方法为其在轻量化设计等方面的应用提供了一定的指导。

基于贝叶斯推断的高斯反卷积信号恢复

针对高斯卷积和白噪声干扰的降质信号,提出一种基于贝叶斯推断的信号恢复模型和数值算法。在模型中引入模型差异项来描述卷积核估计误差,并用高斯分布来描述其统计特性。基于贝叶斯推断,将后验分布解耦为多个推断问题,对信号、模型误差和卷积核参数分别利用原始对偶混合梯度方法、高斯共轭先验法、随机游走的Metropolis算法进行交替更新,有效恢复信号的同时对卷积核参数进行不确定性量化并避免误差传播。数值实验表明,本方法可以同时估计卷积核和恢复信号,性能优于传统的信号恢复方法。

混合动作空间下的多设备边缘计算卸载方法

为降低多设备多边缘服务器场景中设备层级的总成本,并解决现有深度强化学习(deep reinforcement learning,DRL)只支持单一动作空间的算法局限性,提出基于混合决策的多智能体深度确定性策略梯度方法(hybrid-based multi-agent deep determination policy gradient,H-MADDPG)。首先考虑物联网设备/服务器计算能力随负载的动态变化、时变的无线传输信道增益、能量收集的未知性、任务量不确定性多种复杂的环境条件,建立MEC系统模型;其次以一段连续时隙内综合时延、能耗的总成本最小作为优化目标建立问题模型;最后将问题以马尔科夫决策过程(Markov decision procession,MDP)的形式交付给H-MADDPG,在价值网络的辅助下训练并行的两个策略网络,为设备输出离散的服务器选择及连续的任务卸载率。实验结果表明,H-MADDPG方法具有良好的收敛性和稳定性,从计算任务是否密集、延迟是否敏感等不同角度进行观察,H-MADDPG系统整体回报优于Local、OffLoad和DDPG,在计算密集型的任务需求下也能保持更大的系统吞吐量。

A New Hybrid Vertical Coordinate Ocean Model and Its Application in the Simulation of the Changjiang Diluted Water

Based on the analysis of the advantages and disadvantages of some vertical coordinates applied in the calculation of the Changjiang diluted water （CDW）, a new hybrid vertical coordinate is designed, which uses σ coordinate for current and σ-z coordinate for salinity. To combine the current and salinity, the Eulerian-Lagrangian method is used for the salinity calculation, and the baroclinic pressure gradient （BPG） is calculated on the salinity sited layers. The new hybrid vertical coordinate is introduced to the widely used model of POM （Princeton Ocean Model） to make a new model of POM-σ-z. The BPG calculations of an ideal case show that POM-σ-z model brings smaller error than POM model does. The simulations of CDW also show that POM-σ-z model is better than POM model on simulating the salinity and its front.

HYBRID FINITE ANALYTIC SOLUTIONS OF SHALLOW WATER CIRCULATION

The hybrid finite analytic(HFA) method is a kind of numerical scheme in rectangular element. In order to simulate the shallow circulation in irregular bathymetry by HFA scheme, the model in sigma coordinate system was obtained. The model has been tested against three cases: 1) Wind induced circulation; 2) Density driven circulation and 3) Seiche oscillation. The results obtained in the present study compare well with those obtained from the corresponding analytical solutions under idealized for the above three cases. The hybrid finite analytic method and the circulation model in sigma coordinate system can be used calculate the flow and water quality in estuaries and coastal waters.